[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3086":3,"related-3086":76},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":23,"tags":25,"primary_topic":9,"secondary_topics":31,"classification":32,"search_phrases":53,"slug":56,"view_count":57,"doi":58,"paper":59,"created_at":75},3086,"UAV photogrammetry and remote sensing for coastal biodiversity and habitat conservation","https:\u002F\u002Fdoi.org\u002F10.6008\u002Fcbpc2318-2881.2026.001.0002","Coastal ecosystems support high biological diversity while experiencing rapid change caused by shoreline dynamics, sea-level rise, extreme events, pollution, urban development, and intensive resource use. Conventional field surveys provide essential ecological observations but often lack the spatial extent or repetition needed to describe heterogeneous and short-lived coastal conditions. This review evaluates how uncrewed aerial vehicle photogrammetry and remote sensing can support habitat assessment, species monitoring, restoration, and conservation decisions. A structured narrative synthesis was undertaken across studies on image-based mapping, multispectral and thermal observation, laser scanning, direct georeferencing, automated classification, and environmental change detection. The evidence shows that these platforms are most useful when surveys are designed around explicit ecological variables rather than image production alone. RGB imagery and structure-from-motion models provide detailed surface geometry; multispectral, thermal, and laser sensors add information on vegetation condition, moisture, temperature, and three-dimensional structure. Repeated surveys can reveal erosion, inundation, habitat fragmentation, restoration performance, and wildlife distribution at operational scales. Major constraints include variable illumination, water reflectance, wind, tides, positional uncertainty, disturbance risk, regulation, limited training data, and inconsistent validation. Effective programs therefore require standardized timing, field calibration, uncertainty reporting, ethical flight practice, and workflows that convert mapped patterns into management indicators. Future progress depends on sensor integration, explainable automation, interoperable time series, and sustained cooperation between remote-sensing specialists, ecologists, managers, and coastal communities.","沿海生态系统维持着高度的生物多样性，同时经历着由岸线动态、海平面上升、极端事件、污染、城市发展和密集资源利用所引发的快速变化。传统野外调查提供了重要的生态观测，但往往缺乏描述异质性和短生命周期沿海状况所需的空间范围或重复频次。本文综述评估了无人驾驶航空器摄影测量与遥感如何支持栖息地评估、物种监测、恢复和保护决策。我们对基于影像的制图、多光谱与热红外观测、激光扫描、直接地理配准、自动分类和环境变化检测等研究进行了结构化叙述性综合。证据表明，当调查围绕明确的生态变量而非仅以影像生产为目的进行设计时，这些平台最为有用。RGB影像和运动恢复结构（structure-from-motion）模型可提供详细的地表几何信息；多光谱、热红外和激光传感器则补充了植被状况、湿度、温度和三维结构信息。重复调查能够在业务尺度上揭示侵蚀、淹没、栖息地破碎化、恢复成效和野生动物分布。主要制约因素包括光照变化、水体反射、风、潮汐、位置不确定性、干扰风险、法规、训练数据有限以及验证不一致。因此，有效的项目需要标准化的时间安排、野外校准、不确定性报告、符合伦理的飞行实践，以及将制图格局转化为管理指标的工作流程。未来的进展取决于传感器集成、可解释的自动化、可互操作的时间序列，以及遥感专家、生态学家、管理者和沿海社区之间的持续合作。",null,"Nature and Conservation","2026-09-18T00:00:00Z","论文",10,false,64,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":17,"relevant":21,"comment":22},8,18,17,13,1,"综述系统梳理无人机摄影测量与遥感在海岸生境评估、物种监测与修复中的应用与局限，方法学价值明确，但偏生态保护领域，与农业信息化关联间接，属细分方向参考。",[24],{"name":10,"url":6},[26,27,28,29,30],"无人机","遥感","生物多样性","生态监测","海岸生态",[],{"status":33,"locked":14,"version":34,"method":35,"evidence":36},"needs_review","2026-10-22.2","rules",{"candidates":37,"agricultural_evidence":14,"reason":52},[38,46],{"code":39,"score":18,"supported":40,"title":41,"abstract":43,"tags":45},"I3",true,[42],"remote sensing",[27,42,44],"multispectral",[27],{"code":47,"score":20,"supported":40,"title":48,"abstract":50,"tags":51},"H6",[49],"biodiversity",[28],[29,28],"农业相关性需要复核",[54,55],"无人机 摄影测量 海岸生态","UAV 遥感 生物多样性监测","无人机摄影测量海岸生态-3086",3,"10.6008\u002Fcbpc2318-2881.2026.001.0002",{"doi":58,"openalex_id":60,"authors":61,"venue":10,"cited_by_count":65,"oa_url":66,"card":67,"direction":73,"ingested_from":74},"W7213604527",[62],{"name":63,"orcid":64},"Murat Yakar","https:\u002F\u002Forcid.org\u002F0000-0002-2664-6251",0,"https:\u002F\u002Fwww.natureandconservation.com\u002Findex.php\u002Fnature\u002Farticle\u002Fdownload\u002F8949\u002F5099",{"tldr":68,"method":69,"finding":70,"direction":71,"opportunity":72},"综述无人机摄影测量与遥感在海岸生物多样性和栖息地保护中的应用与局限。","结构化叙述性综述，涵盖RGB、多光谱、热红外、激光扫描及自动分类等研究。","围绕明确生态变量设计调查时最有效，但受光照、风潮、法规和验证不一致等制约。","农业遥感与作物表型","可探索多传感器融合与可解释自动化，构建标准化时间序列以支撑海岸管理指标。","数字乡村与农业信息化","openalex","2026-09-21T23:30:37.840826Z",{"total":77,"page":21,"page_size":77,"items":78},6,[79,188,250,311,390,440],{"id":80,"title":81,"url":82,"summary":83,"summary_zh":84,"content":9,"source_name":85,"source_url":82,"published_at":86,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":87,"score_detail":88,"sources":93,"tags":95,"primary_topic":99,"secondary_topics":105,"classification":111,"search_phrases":140,"slug":143,"view_count":65,"doi":144,"paper":145,"created_at":187},5567,"Capturing Spatial Heterogeneity Using UAV-Derived Multispectral Phenotypes and Random Regression Models Improves Genetic Evaluation in Alfalfa Breeding Trials","https:\u002F\u002Fdoi.org\u002F10.64898\u002F2026.10.14.759249","Spatial heterogeneity is a major source of environmental noise in field trials, reducing genetic evaluation accuracy. Conventional spatial correction methods use field layout and plot proximity but often fail to capture in-season dynamics or leverage temporal data from high-throughput phenotyping. We hypothesized that longitudinal UAV-derived multispectral measurements could characterize field-scale environmental heterogeneity and improve genetic evaluation in breeding trials. Multispectral imagery was collected throughout the growing season across eight alfalfa trials in New York and New Mexico (20222023). Vegetation indices were evaluated for heritability and association with biomass yield using univariate and multi-trait models. Longitudinal measurements were modeled with random regression (RR) to estimate plot-specific permanent environmental effects (PEE), incorporated into genomic mixed models within a two-stage framework. Model performance was compared with conventional autoregressive (AR) and baseline (no correction) models using metrics of consistency and accuracy of estimated genetic effects across replicates. Vegetation indices showed moderate to high heritability, with GNDVI consistently outperforming NDVI and NDRE. Both univariate and multi-trait analyses revealed substantial spatial variability, particularly in New Mexico trials. RR-derived PEE captured field-scale spatial patterns and improved genetic signal recovery relative to baseline and AR models, with the largest gains under strong spatial heterogeneity and comparable performance where variability was limited. The RR framework improved reproducibility of genetic effect estimates across independent field subsets without compromising agreement with full-trial evaluations. These findings show that integrating UAV data via random regression enhances genetic signal recovery and selection accuracy, with broader potential for precision agriculture and biosensor-driven crop management.","空间异质性是田间试验中环境噪声的主要来源，会降低遗传评估的准确性。传统空间校正方法利用田间布局和小区邻近性，但往往无法捕捉生育期内的动态变化，也未能充分利用高通量表型分析产生的时间序列数据。我们假设，基于无人机（UAV）的纵向多光谱测量能够表征田块尺度的环境异质性，并改善育种试验中的遗传评估。在2022—2023年纽约州和新墨西哥州的8个紫花苜蓿试验中，于整个生长季收集了多光谱影像。采用单性状和多性状模型评估了植被指数的遗传力及其与生物量产量的关联。纵向测量数据采用随机回归（RR）建模，以估计小区特异性永久环境效应（PEE），并在两阶段框架中将其纳入基因组混合模型。利用重复间估计遗传效应的一致性和准确性指标，将模型性能与传统的自回归（AR）模型和基线（无校正）模型进行比较。植被指数表现出中到高的遗传力，其中GNDVI始终优于NDVI和NDRE。单性状和多性状分析均揭示了显著的空间变异，尤其在新墨西哥州的试验中更为明显。RR衍生的PEE捕捉到了田块尺度的空间格局，相对于基线模型和AR模型改善了遗传信号的恢复，在强空间异质性条件下增益最大，而在变异有限时性能相当。RR框架提高了遗传效应估计在独立田间子集间的可重复性，同时未损害与全试验评估的一致性。这些结果表明，通过随机回归整合UAV数据可增强遗传信号恢复和选择准确性，在精准农业和生物传感器驱动的作物管理方面具有更广泛的潜力。","bioRxiv (Cold Spring Harbor Laboratory)","2026-10-20T00:00:00Z",79,{"impact":18,"substance":89,"depth":18,"authority":90,"freshness":91,"relevant":21,"comment":92},22,12,9,"该研究利用无人机多光谱表型与随机回归模型校正田间空间异质性，显著提升苜蓿育种遗传评估精度，方法新颖、数据覆盖两州八组试验，对智慧育种有实质参考价值。",[94],{"name":85,"url":82},[96,26,97,27,98],"智慧农业","育种","紫花苜蓿",{"code":100,"slug":101,"name":102,"path":103,"parent_code":104},"A7","forage-cropping","饲草与耕作制度","\u002Ftopic\u002Ffield-crops\u002Fforage-cropping","A",[106],{"code":39,"slug":107,"name":108,"path":109,"parent_code":110},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",{"status":112,"locked":14,"version":34,"method":35,"evidence":113},"classified",{"candidates":114,"agricultural_evidence":40},[115,122,126,134],{"code":100,"score":116,"supported":40,"title":117,"abstract":119,"tags":121},15,[118],"alfalfa",[120,118],"苜蓿",[98],{"code":39,"score":90,"supported":40,"title":123,"abstract":124,"tags":125},[44],[44],[27],{"code":127,"score":128,"supported":40,"title":129,"abstract":130,"tags":133},"I1",7,[],[131,132],"精准农业","precision agriculture",[96],{"code":135,"score":136,"supported":40,"title":137,"abstract":138,"tags":139},"C4",4,[],[120],[98],[141,142],"无人机 多光谱 苜蓿 育种","随机回归 空间异质性 遗传评估","无人机多光谱苜蓿育种-5567","10.64898\u002F2026.10.14.759249",{"doi":144,"openalex_id":146,"authors":147,"venue":85,"cited_by_count":65,"oa_url":82,"card":182,"direction":71,"ingested_from":74},"W7223083966",[148,151,154,157,160,162,164,167,170,173,176,179],{"name":149,"orcid":150},"Dinesh Ghimire","https:\u002F\u002Forcid.org\u002F0009-0008-2281-1396",{"name":152,"orcid":153},"Ranjita Thapa","https:\u002F\u002Forcid.org\u002F0000-0001-6116-7611",{"name":155,"orcid":156},"Karl H. Kunze","https:\u002F\u002Forcid.org\u002F0000-0003-4548-1808",{"name":158,"orcid":159},"Felipe Sabadin","https:\u002F\u002Forcid.org\u002F0000-0003-2937-1465",{"name":161,"orcid":9},"Julie Hansen",{"name":163,"orcid":9},"Christopher Pierce",{"name":165,"orcid":166},"Liam Wickes-Do","https:\u002F\u002Forcid.org\u002F0009-0003-2927-4590",{"name":168,"orcid":169},"Nicholas Santantonio","https:\u002F\u002Forcid.org\u002F0000-0002-4351-4023",{"name":171,"orcid":172},"Ian M. Ray","https:\u002F\u002Forcid.org\u002F0000-0003-2056-536X",{"name":174,"orcid":175},"Michael A. Gore","https:\u002F\u002Forcid.org\u002F0000-0001-6896-8024",{"name":177,"orcid":178},"Virginia Mae Moore","https:\u002F\u002Forcid.org\u002F0000-0001-7888-3366",{"name":180,"orcid":181},"Kelly R. Robbins","https:\u002F\u002Forcid.org\u002F0000-0001-9522-9585",{"tldr":183,"method":184,"finding":185,"direction":71,"opportunity":186},"用无人机多光谱时序表型和随机回归模型校正田间空间异质性，提升苜蓿育种遗传评估精度。","八项苜蓿试验的无人机多光谱植被指数，随机回归估计小区永久环境效应并嵌入基因组混合","随机回归提取的环境效应能捕捉田块尺度空间格局，在强异质性下显著提升遗传信号恢复与选择准确性。","可将无人机时序表型与随机回归框架推广到其他作物育种试验，并探索多源遥感与基因组选择的联合建模。","2026-10-22T11:20:32.655394Z",{"id":189,"title":190,"url":191,"summary":192,"summary_zh":9,"content":9,"source_name":193,"source_url":191,"published_at":194,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":195,"score_detail":196,"sources":201,"tags":203,"primary_topic":206,"secondary_topics":210,"classification":211,"search_phrases":232,"slug":235,"view_count":21,"doi":236,"paper":237,"created_at":249},4973,"Barley head detection using UAV imagery and YOLOv10","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs11119-026-10468-w","Barley head detection using UAV imagery and YOLOv10。Precision Agriculture","Precision Agriculture","2026-10-14T00:00:00Z",75,{"impact":197,"substance":198,"depth":19,"authority":199,"freshness":17,"relevant":21,"comment":200},16,20,14,"将YOLOv10用于无人机大麦穗检测，方法新颖、数据可靠，属细分领域技术进展，值得精选。",[202],{"name":193,"url":191},[96,26,204,27,205],"农业人工智能","大麦",{"code":127,"slug":207,"name":208,"path":209,"parent_code":110},"smart-agriculture","智慧农业总论","\u002Ftopic\u002Fdigital-agriculture\u002Fsmart-agriculture",[],{"status":112,"locked":14,"version":34,"method":35,"evidence":212},{"candidates":213,"agricultural_evidence":40},[214,218,223,228],{"code":127,"score":136,"supported":40,"title":215,"abstract":216,"tags":217},[],[132],[96],{"code":219,"score":21,"supported":14,"title":220,"abstract":221,"tags":222},"A5",[],[],[205],{"code":224,"score":21,"supported":14,"title":225,"abstract":226,"tags":227},"I2",[],[],[204],{"code":39,"score":21,"supported":14,"title":229,"abstract":230,"tags":231},[],[],[27],[233,234],"无人机 大麦 穗检测","YOLOv10 大麦 遥感","无人机大麦穗检测-4973","10.1007\u002Fs11119-026-10468-w",{"doi":236,"openalex_id":238,"authors":239,"venue":193,"cited_by_count":65,"oa_url":9,"card":9,"direction":9,"ingested_from":74},"W7221758985",[240,243,246],{"name":241,"orcid":242},"Ameer Tamoor Khan","https:\u002F\u002Forcid.org\u002F0000-0001-6838-992X",{"name":244,"orcid":245},"Saiful Azim","https:\u002F\u002Forcid.org\u002F0000-0002-2686-7150",{"name":247,"orcid":248},"Signe Marie Jensen","https:\u002F\u002Forcid.org\u002F0000-0002-7738-5231","2026-10-15T23:30:02.421798Z",{"id":251,"title":252,"url":253,"summary":254,"summary_zh":255,"content":9,"source_name":256,"source_url":253,"published_at":257,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":258,"score_detail":259,"sources":261,"tags":263,"primary_topic":9,"secondary_topics":267,"classification":268,"search_phrases":284,"slug":287,"view_count":65,"doi":288,"paper":289,"created_at":310},4610,"UAV-based estimation of aboveground biomass carbon in the Sundarbans mangrove forest of Bangladesh","https:\u002F\u002Fdoi.org\u002F10.1088\u002F2515-7620\u002Faeb18f","Abstract Reliable biomass and carbon assessments are essential for understanding mangrove forest dynamics and their role in global carbon cycling. However, field-based biomass estimation is time-consuming, labour-intensive, and costly. Remote sensing provides a practical and cost-effective alternative, particularly with unmanned aerial vehicles (UAVs) that can capture high-resolution imagery. The assessment of biomass carbon in the Sundarbans Mangrove Forest (SMF) using high-resolution remote sensing remains understudied. This study develops an integrated carbon assessment framework for estimating aboveground biomass carbon in the SMF using UAV-derived canopy metrics and field measurements. Error propagation and feature importance analyses were performed to quantify uncertainty and assess predictor influence, providing a robust and uncertainty-aware approach for mangrove biomass carbon assessment. CPA was used to estimate proxy DBH using linear, polynomial, logarithmic, power, and exponential regression models. All CPA–DBH models showed comparable performance (R2 = 0.73-0.75). The linear and polynomial models achieved the lowest RMSE (1.77 cm), while the polynomial model produced the lowest MAE (1.22 cm) and was therefore selected for estimating Proxy DBH. Field-based AGBC was calculated from measured tree height and DBH using species-specific and generic allometric equations applicable to the Sundarbans, whereas UAV-based AGBC was estimated using UAV-derived Mean DSM Height and CPA-derived Proxy DBH. Simple linear regression (SLR), multiple linear regression (MLR), and machine-learning (ML) models (Random Forest, Support Vector Machine, Artificial Neural Network, and Extreme Gradient Boosting) were evaluated for AGBC prediction. The MLR model showed the best performance on the validation\u002Fmodel-selection subset (R² = 0.86, RMSE = 14.70 Mg C ha⁻¹, MedAE = 4.58 Mg C ha⁻¹). UAV-derived AGBC estimates closely matched field measurements and previously reported values for the Sundarbans, demonstrating the potential of high-resolution UAV metrics for reliable mangrove AGBC estimation and supporting carbon monitoring, REDD+ initiatives, and sustainable mangrove management in Bangladesh.","可靠的生物量和碳评估对于理解红树林动态及其在全球碳循环中的作用至关重要。然而，基于实地调查的生物量估算耗时、费力且成本高昂。遥感提供了一种实用且具有成本效益的替代方案，尤其是利用能够获取高分辨率影像的无人机（UAV）。利用高分辨率遥感对孙德尔本斯红树林（SMF）进行生物量碳评估的研究仍较为缺乏。本研究开发了一个综合碳评估框架，利用无人机获取的冠层指标和实地测量数据估算SMF的地上生物量碳。通过误差传播和特征重要性分析量化不确定性并评估预测因子的影响，为红树林生物量碳评估提供了一种稳健且考虑不确定性的方法。采用CPA（冠层投影面积）通过线性、多项式、对数、幂函数和指数回归模型估算代理DBH（胸径）。所有CPA–DBH模型均表现出相近的性能（R² = 0.73–0.75）。线性模型和多项式模型取得了最低的RMSE（1.77 cm），而多项式模型产生了最低的MAE（1.22 cm），因此被选用于估算代理DBH。基于实地的AGBC（地上生物量碳）通过实测树高和DBH，采用适用于孙德尔本斯的物种特异性异速生长方程和通用异速生长方程计算，而基于无人机的AGBC则利用无人机获取的平均DSM高度和CPA衍生的代理DBH进行估算。采用简单线性回归（SLR）、多元线性回归（MLR）和机器学习（ML）模型（随机森林、支持向量机、人工神经网络和极端梯度提升）评估AGBC预测性能。MLR模型在验证\u002F模型选择子集上表现最佳（R² = 0.86，RMSE = 14.70 Mg C ha⁻¹，MedAE = 4.58 Mg C ha⁻¹）。无人机获取的AGBC估算值与实地测量结果及先前报道的孙德尔本斯数值高度吻合，表明高分辨率无人机指标在可靠估算红树林AGBC方面具有潜力，可支持孟加拉国的碳监测、REDD+倡议和可持续红树林管理。","Environmental Research Communications","2026-10-07T00:00:00Z",80,{"impact":18,"substance":89,"depth":18,"authority":199,"freshness":17,"relevant":21,"comment":260},"该研究利用无人机高分辨率影像与机器学习模型估算孟加拉国孙德尔本斯红树林地上生物量碳，方法新颖、数据可靠，对林业碳汇监测与REDD+具有参考价值，但属境外案例、应用范围有限，可作为专业资讯收录。",[262],{"name":256,"url":253},[26,27,264,265,266],"智慧林业","红树林","碳汇计量",[],{"status":33,"locked":14,"version":34,"method":35,"evidence":269},{"candidates":270,"agricultural_evidence":14,"reason":52},[271,275,280],{"code":39,"score":128,"supported":40,"title":272,"abstract":273,"tags":274},[],[27,42],[27],{"code":224,"score":57,"supported":40,"title":276,"abstract":277,"tags":279},[],[278],"机器学习",[],{"code":47,"score":21,"supported":14,"title":281,"abstract":282,"tags":283},[],[],[265],[285,286],"孟加拉国 孙德尔本斯 红树林 无人机","无人机 生物量碳 遥感估算","孟加拉国孙德尔本斯红树林无人机-4610","10.1088\u002F2515-7620\u002Faeb18f",{"doi":288,"openalex_id":290,"authors":291,"venue":256,"cited_by_count":65,"oa_url":303,"card":304,"direction":309,"ingested_from":74},"W7220935167",[292,295,298,300],{"name":293,"orcid":294},"Md Abul Hashem","https:\u002F\u002Forcid.org\u002F0009-0006-2712-3168",{"name":296,"orcid":297},"Md Sarwar Hossain","https:\u002F\u002Forcid.org\u002F0000-0001-6101-659X",{"name":299,"orcid":9},"Md. Tauhidor Rahaman",{"name":301,"orcid":302},"Brian Barrett","https:\u002F\u002Forcid.org\u002F0000-0002-4380-4020","https:\u002F\u002Fiopscience.iop.org\u002Farticle\u002F10.1088\u002F2515-7620\u002Faeb18f\u002Fpdf",{"tldr":305,"method":306,"finding":307,"direction":71,"opportunity":308},"利用无人机冠层指标与地面数据估算孟加拉孙德尔本斯红树林地上生物量碳。","无人机高分辨率影像提取冠层指标，结合CPA估算DBH，用MLR与机器学习模型预测","多元线性回归表现最佳（R²=0.86），无人机估算值与实测及历史值高度吻合。","可探索多时相无人机与卫星融合，提升红树林碳储量动态监测及不确定性量化能力。","农业绿色发展与碳","2026-10-10T23:31:07.399747Z",{"id":312,"title":313,"url":314,"summary":315,"summary_zh":316,"content":9,"source_name":317,"source_url":314,"published_at":318,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":319,"score_detail":320,"sources":322,"tags":324,"primary_topic":326,"secondary_topics":327,"classification":332,"search_phrases":363,"slug":366,"view_count":65,"doi":367,"paper":368,"created_at":389},4505,"Application of Deep Learning Techniques for Vineyard Mapping and Characterization Using Multispectral UAV Imagery: A Case Study from Northern Greece","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23232412","Developments in unmanned aerial vehicle (UAV) technology and the rise of Deep Learning (DL) have drastically boosted the capacities of precision viticulture to monitor the environments of farms in a scalable and detailed manner. Vineyard mappings and extraction of vine-specific features are crucial for yield estimation, plant health assessment, and management decisions. This study examines the application of Deep Learning-based image analysis techniques for vineyard mapping and characterization using multispectral UAV image data (Deep Learning). The proposed approach integrates spatial and spectral information to delineate vineyard structures and capture key canopy characteristics relevant to precision agriculture. Multimodal datasets, including RGB and multispectral bands, facilitate improved discrimination between vines and surrounding elements. Geospatial analysis and data processing are further performed through ArcGIS Pro, enabling spatial validation of results, identifying extracted features, and creating appropriate interpretative thematic layers. The combination of UAV imagery, DL tools and GIS based analysis offers a strong and flexible workflow that can be adjusted for different grape cultivars, growth conditions and circumstances. Overall, the study demonstrates the potential benefits of the rigid union of UAV-based remote sensing with Deep Learning and GIS techniques to create a flexible, operational, and data-driven framework for vineyard management. Based on this, sustainable and fruitful management practices can be elaborated, thus optimizing resources and vineyard management decisions in precision viticulture,underlining the importance of such a technological integration for monitoring and managing modern vineyards.","无人机（UAV）技术的发展与深度学习（DL）的兴起，极大地提升了精准葡萄栽培以可扩展且精细的方式监测农场环境的能力。葡萄园制图与葡萄植株特异性特征的提取对于产量估算、植株健康评估和管理决策至关重要。本研究探讨了基于深度学习的图像分析技术在多光谱无人机影像数据（深度学习）用于葡萄园制图与表征中的应用。所提出的方法整合了空间与光谱信息，以 delineate 葡萄园结构并捕捉与精准农业相关的关键冠层特征。多模态数据集（包括RGB和多光谱波段）有助于更好地区分葡萄藤与周围元素。通过ArcGIS Pro进一步进行地理空间分析与数据处理，从而实现结果的空间验证、提取特征的识别以及适当的解释性专题图层的创建。无人机影像、深度学习工具与基于GIS的分析相结合，提供了一套强大且灵活的工作流程，可针对不同葡萄品种、生长条件和环境进行调整。总体而言，本研究展示了将基于无人机的遥感与深度学习和GIS技术 rigidly 结合，以创建灵活、可操作且数据驱动的葡萄园管理框架的潜在优势。在此基础上，可制定可持续且富有成效的管理实践，从而优化精准葡萄栽培中的资源与葡萄园管理决策，凸显了这种技术整合对现代葡萄园监测与管理的重要性。","Zenodo (CERN European Organization for Nuclear Research)","2026-10-08T00:00:00Z",68,{"impact":90,"substance":18,"depth":197,"authority":20,"freshness":91,"relevant":21,"comment":321},"希腊北部葡萄园的多光谱无人机深度学习制图案例，方法新颖但属区域性研究，对国内智慧农业有参考价值。",[323],{"name":317,"url":314},[96,26,204,27,325],"葡萄园管理",{"code":39,"slug":107,"name":108,"path":109,"parent_code":110},[328],{"code":224,"slug":329,"name":330,"path":331,"parent_code":110},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",{"status":112,"locked":14,"version":34,"method":35,"evidence":333},{"candidates":334,"agricultural_evidence":40},[335,339,345,349,356],{"code":39,"score":18,"supported":40,"title":336,"abstract":337,"tags":338},[44],[27,42,44],[27],{"code":224,"score":116,"supported":40,"title":340,"abstract":342,"tags":344},[341],"deep learning",[343,341],"深度学习",[204],{"code":127,"score":128,"supported":40,"title":346,"abstract":347,"tags":348},[],[131,132],[96],{"code":350,"score":77,"supported":40,"title":351,"abstract":352,"tags":355},"B2",[],[353,354],"葡萄","grape",[],{"code":357,"score":77,"supported":40,"title":358,"abstract":359,"tags":362},"I4",[],[360,361],"空间分析","GIS",[],[364,365],"希腊 葡萄园 无人机 深度学习","多光谱 UAV 葡萄园 制图","希腊葡萄园无人机深度学习-4505","10.5281\u002Fzenodo.23232412",{"doi":367,"openalex_id":369,"authors":370,"venue":317,"cited_by_count":65,"oa_url":314,"card":384,"direction":71,"ingested_from":74},"W7220993218",[371,373,376,378,381],{"name":372,"orcid":9},"Dimitra Passa",{"name":374,"orcid":375},"Spyridon E. Detsikas","https:\u002F\u002Forcid.org\u002F0000-0002-3960-9361",{"name":377,"orcid":9},"Georgios Nektarios Tselos",{"name":379,"orcid":380},"George P. Petropoulos","https:\u002F\u002Forcid.org\u002F0000-0003-1442-1423",{"name":382,"orcid":383},"Antonis Litke","https:\u002F\u002Forcid.org\u002F0000-0002-7658-2559",{"tldr":385,"method":386,"finding":387,"direction":71,"opportunity":388},"用多光谱无人机影像和深度学习实现葡萄园制图与冠层特征提取。","多光谱无人机影像、RGB与多光谱融合、深度学习、ArcGIS Pro空间分析。","UAV+DL+GIS可灵活构建葡萄园精准管理的数据驱动框架。","可探索跨品种、跨生长阶段的迁移学习与轻量化模型，提升葡萄园实时监测泛化能力。","2026-10-09T23:30:25.912664Z",{"id":391,"title":392,"url":393,"summary":394,"summary_zh":395,"content":9,"source_name":396,"source_url":393,"published_at":397,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":398,"sources":400,"tags":402,"primary_topic":9,"secondary_topics":404,"classification":405,"search_phrases":421,"slug":424,"view_count":21,"doi":425,"paper":426,"created_at":439},4300,"Road surface material classification from unmanned aerial vehicle red, green, blue and multispectral imagery using spectral features and ensemble machine learning model","https:\u002F\u002Fdoi.org\u002F10.12913\u002F22998624\u002F226062","The aim of this study was to evaluate the impact of incorporating multispectral data acquired from an unmanned aerial vehicle (UAV) as an extension of standard RGB imagery on the accuracy of road surface material classification using machine learning algorithms.The research was conducted on six road sections differing in pavement type and technical condition.The performance of three ensemble learning algorithms: Random Forest, XGBoost, and LightGBM was investigated.Three input data configurations were considered: RGB imagery, a seven-band dataset comprising RGB channels and four multispectral bands (MS7), and an extended dataset (MS9) additionally including the NDVI and NDRE vegetation indices.For the best-performing model, the influence of texture features calculated within a moving 5 × 5 pixel window and edge information derived using the Sobel operator was also evaluated.The results demonstrated that the inclusion of multispectral data significantly improved classification performance compared with RGB imagery alone.The highest accuracy was achieved by the LightGBM model using the MS9 dataset, reaching an overall accuracy (OA) of 81.31%.The incorporation of texture features further increased the classification accuracy to 93.30% OA.The findings confirm the positive contribution of UAVacquired multispectral data to the effectiveness of road surface material classification.","本研究旨在评估将无人机（UAV）获取的多光谱数据作为标准RGB影像的扩展，对基于机器学习算法的路面材料分类精度的影响。研究在六段路面类型和技术状况不同的道路上进行，考察了三种集成学习算法——随机森林（Random Forest）、XGBoost和LightGBM的性能。研究考虑了三种输入数据配置：RGB影像、包含RGB通道和四个多光谱波段的七波段数据集（MS7），以及额外加入NDVI和NDRE植被指数的扩展数据集（MS9）。对于表现最佳的模型，还评估了在5×5像素移动窗口内计算的纹理特征以及利用Sobel算子提取的边缘信息的影响。结果表明，与仅使用RGB影像相比，加入多光谱数据显著提升了分类性能。使用MS9数据集的LightGBM模型取得了最高精度，总体精度（OA）达到81.31%。引入纹理特征后，分类精度进一步提升至93.30% OA。研究结果证实了无人机获取的多光谱数据对路面材料分类效果具有积极贡献。","Advances in Science and Technology – Research Journal","2026-10-04T00:00:00Z",{"impact":17,"substance":198,"depth":197,"authority":90,"freshness":17,"relevant":21,"comment":399},"该研究利用无人机多光谱影像与集成机器学习提升路面材料分类精度，方法新颖、数据详实，对农业遥感与智慧农业有参考价值，但属细分领域论文，公共影响有限。",[401],{"name":396,"url":393},[96,26,204,27,403],"路面分类",[],{"status":33,"locked":14,"version":34,"method":35,"evidence":406},{"candidates":407,"agricultural_evidence":14,"reason":52},[408,413,417],{"code":224,"score":116,"supported":40,"title":409,"abstract":411,"tags":412},[410],"machine learning",[278,410],[204],{"code":39,"score":90,"supported":40,"title":414,"abstract":415,"tags":416},[44],[44],[27],{"code":127,"score":21,"supported":14,"title":418,"abstract":419,"tags":420},[],[],[96],[422,423],"无人机 多光谱 路面材料 分类","LightGBM 道路 遥感 分类","无人机多光谱路面材料分类-4300","10.12913\u002F22998624\u002F226062",{"doi":425,"openalex_id":427,"authors":428,"venue":396,"cited_by_count":65,"oa_url":432,"card":433,"direction":438,"ingested_from":74},"W7219783179",[429],{"name":430,"orcid":431},"Marcin Bilski","https:\u002F\u002Forcid.org\u002F0000-0002-1445-5749","https:\u002F\u002Fwww.astrj.com\u002Fpdf-226062-148948?filename=Road-surface-material-cla.pdf",{"tldr":434,"method":435,"finding":436,"direction":71,"opportunity":437},"用无人机RGB与多光谱影像结合集成机器学习，分类路面材料并验证多光谱与纹理特征的增益。","无人机RGB\u002F多光谱影像、NDVI\u002FNDRE、纹理与Sobel边缘，随机森林\u002FX","多光谱数据显著优于纯RGB，LightGBM用MS9达81.31%，加入纹理特征后提升至93.30%","可迁移到农田道路、机耕道与田间通行性识别，结合时序影像与轻量模型服务农机导航。","智慧农业 \u002F 农业物联网","2026-10-06T23:30:09.562617Z",{"id":441,"title":442,"url":443,"summary":444,"summary_zh":445,"content":9,"source_name":446,"source_url":443,"published_at":447,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":65,"score_detail":448,"sources":450,"tags":452,"primary_topic":9,"secondary_topics":454,"classification":455,"search_phrases":477,"slug":480,"view_count":65,"doi":9,"paper":481,"created_at":501},4220,"Системи на основі YOLO для виявлення вибухонебезпечних залишків війни: огляд для гуманітарного розмінування","https:\u002F\u002Fais.khpi.edu.ua\u002Farticle\u002Fview\u002F372728","Relevance. Humanitarian demining remains a critical safety challenge because explosive remnants of war, landmines, and unexploded ordnance continue to endanger civilians and hinder post-conflict recovery. In this context, artificial intelligence and computer vision are increasingly used to support hazardous-object detection in UAVs-based survey operations. The purpose of this paper is to synthesize contemporary approaches to the detection of landmines, unexploded ordnance, and other explosive remnants of war, and to define the role of YOLO models in an integrated technological pipeline for surveying hazardous areas. The object of the review is technological solutions for detecting hazardous objects using UAVs, sensor systems, geoinformation technologies, and YOLO-family models. The subject of the paper is the application of YOLO-based systems in integrated pipelines for localizing potential explosive objects from visual and spectral data. Research results. The review shows that modern approaches combine ground-penetrating radar, magnetometry, thermal sensing, multispectral imaging, hyperspectral imaging, UAV-based inspection, GIS platforms, and edge\u002Fcloud architectures. YOLO demonstrates strong potential for rapid localization of visually or spectrally expressed indicators of hazardous objects, especially when combined with other sensing modalities. Conclusions. At the same time, YOLO cannot serve as the sole basis for declaring an area safe. Its use is constrained by small target size, camouflage, domain shift, limited annotated datasets, and the high cost of false negatives. Therefore, YOLO-based detection should be treated as one component of a multisensor, human-supervised decision-support system.","相关性。人道主义排雷仍然是一项关键的安全挑战，因为战争遗留爆炸物、地雷和未爆弹药继续危及平民并阻碍冲突后恢复。在此背景下，人工智能和计算机视觉越来越多地被用于支持基于无人机（UAV）的勘察作业中的危险物体检测。本文旨在综合当前对地雷、未爆弹药和其他战争遗留爆炸物的检测方法，并明确YOLO模型在危险区域勘察集成技术流程中的作用。综述的对象是利用无人机、传感器系统、地理信息技术和YOLO系列模型检测危险物体的技术解决方案。论文的主题是基于YOLO的系统在集成流程中的应用，用于从视觉和光谱数据中定位潜在爆炸物体。研究结果。综述表明，现代方法结合了探地雷达、磁力测量、热传感、多光谱成像、高光谱成像、基于无人机的检查、GIS平台以及边缘\u002F云架构。YOLO在快速定位视觉或光谱上可表现出的危险物体指标方面展现出强大潜力，尤其是与其他传感模态结合使用时。结论。与此同时，YOLO不能作为宣布某一区域安全的唯一依据。其使用受到小目标尺寸、伪装、域偏移、标注数据集有限以及假阴性高成本的制约。因此，基于YOLO的检测应被视为多传感器、人工监督决策支持系统的一个组成部分。","The Scientific Issues of Ternopil Volodymyr Hnatiuk National Pedagogical University Series pedagogy","2026-10-02T00:00:00Z",{"impact":65,"substance":65,"depth":65,"authority":65,"freshness":65,"relevant":65,"comment":449},"主题为人道主义排雷中的YOLO目标检测综述，属军事\u002F人道安全领域，与三农及农业信息化无直接关联，不予入选。",[451],{"name":446,"url":443},[26,204,453,27],"目标检测",[],{"status":33,"locked":14,"version":34,"method":35,"evidence":456},{"candidates":457,"agricultural_evidence":14,"reason":52},[458,467,472],{"code":224,"score":459,"supported":40,"title":460,"abstract":462,"tags":466},19,[461],"YOLO",[463,464,465,461],"计算机视觉","artificial intelligence","computer vision",[204,453],{"code":39,"score":128,"supported":40,"title":468,"abstract":469,"tags":471},[],[44,470],"hyperspectral",[27],{"code":357,"score":77,"supported":40,"title":473,"abstract":474,"tags":476},[],[475,361],"地理信息",[],[478,479],"YOLO 排雷 无人机","人道主义排雷 遥感","YOLO排雷无人机-4220",{"doi":9,"openalex_id":482,"authors":483,"venue":446,"cited_by_count":65,"oa_url":443,"card":496,"direction":438,"ingested_from":74},"W7218146160",[484,486,489,491,494],{"name":485,"orcid":9},"Andrii Podorozhniak",{"name":487,"orcid":488},"Денис Левченко","https:\u002F\u002Forcid.org\u002F0009-0005-4102-6236",{"name":490,"orcid":9},"Nataliia Liubchenko",{"name":492,"orcid":493},"Іслам Ісламов","https:\u002F\u002Forcid.org\u002F0000-0001-8645-0640",{"name":495,"orcid":9},"Oleksandr Skorlupin",{"tldr":497,"method":498,"finding":499,"direction":71,"opportunity":500},"综述YOLO模型在无人机人道排雷中检测战争爆炸残留物的应用与局限。","综述探地雷达、磁力、热成像、多\u002F高光谱、无人机、GIS与YOLO集成方案。","YOLO可快速定位危险目标，但受小目标、伪装、域偏移、数据少和高漏检代价限制，不能单独判定区域安全。","可探索多源遥感与YOLO融合的轻量边缘检测，并构建农业场景下小目标、域偏移的鲁棒性评估基准。","2026-10-05T23:30:12.187482Z"]